#include "connected_layer.h"
#include "convolutional_layer.h"
#include "batchnorm_layer.h"
#include "utils.h"
#include "opencl.h"
#include "blas.h"
#include "gemm.h"

#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam)
{
	int i;
	layer l = {0};
	l.learning_rate_scale = 1;
	l.type = CONNECTED;

	l.inputs = inputs;
	l.outputs = outputs;
	l.batch=batch;
	l.batch_normalize = batch_normalize;
	l.h = 1;
	l.w = 1;
	l.c = inputs;
	l.out_h = 1;
	l.out_w = 1;
	l.out_c = outputs;
    l.n = l.out_c;
    l.size = 1;
    l.stride = l.stride_x = l.stride_y = 1;
    l.pad = 0;
    l.activation = activation;
    l.learning_rate_scale = 1;
    l.groups = 1;
    l.dilation = 1;

	l.output = (float*)calloc(batch*outputs, sizeof(float));
	l.delta = (float*)calloc(batch*outputs, sizeof(float));

	l.weight_updates = (float*)calloc(inputs*outputs, sizeof(float));
	l.bias_updates = (float*)calloc(outputs, sizeof(float));

	l.weights = (float*)calloc(outputs*inputs, sizeof(float));
	l.biases = (float*)calloc(outputs, sizeof(float));

	l.forward = forward_connected_layer;
	l.backward = backward_connected_layer;
	l.update = update_connected_layer;

	//float scale = 1./sqrt(inputs);
	float scale = sqrt(2./inputs);
	for(i = 0; i < outputs*inputs; ++i){
		l.weights[i] = scale*rand_uniform(-1, 1);
	}

	for(i = 0; i < outputs; ++i){
		l.biases[i] = 0;
	}

	if(adam){
		l.m = (float*)calloc(l.inputs*l.outputs, sizeof(float));
		l.v = (float*)calloc(l.inputs*l.outputs, sizeof(float));
		l.bias_m = (float*)calloc(l.outputs, sizeof(float));
		l.scale_m = (float*)calloc(l.outputs, sizeof(float));
		l.bias_v = (float*)calloc(l.outputs, sizeof(float));
		l.scale_v = (float*)calloc(l.outputs, sizeof(float));
	}
	if(batch_normalize){
		l.scales = (float*)calloc(outputs, sizeof(float));
		l.scale_updates = (float*)calloc(outputs, sizeof(float));
		for(i = 0; i < outputs; ++i){
			l.scales[i] = 1;
		}

		l.mean = (float*)calloc(outputs, sizeof(float));
		l.mean_delta = (float*)calloc(outputs, sizeof(float));
		l.variance = (float*)calloc(outputs, sizeof(float));
		l.variance_delta = (float*)calloc(outputs, sizeof(float));

		l.rolling_mean = (float*)calloc(outputs, sizeof(float));
		l.rolling_variance = (float*)calloc(outputs, sizeof(float));

		l.x = (float*)calloc(batch*outputs, sizeof(float));
		l.x_norm = (float*)calloc(batch*outputs, sizeof(float));
	}

#ifdef GPU
	if (gpu_index >= 0) {
		l.forward_gpu = forward_connected_layer_gpu;
		l.backward_gpu = backward_connected_layer_gpu;
		l.update_gpu = update_connected_layer_gpu;

		l.weights_gpu = opencl_make_array(l.weights, outputs * inputs);
		l.biases_gpu = opencl_make_array(l.biases, outputs);

		l.weight_updates_gpu = opencl_make_array(l.weight_updates, outputs * inputs);
		l.bias_updates_gpu = opencl_make_array(l.bias_updates, outputs);

		l.output_gpu = opencl_make_array(l.output, outputs * batch);
		l.delta_gpu = opencl_make_array(l.delta, outputs * batch);
		if (adam) {
			l.m_gpu = opencl_make_array(l.m, inputs * outputs);
			l.v_gpu = opencl_make_array(l.v, inputs * outputs);
			l.bias_m_gpu = opencl_make_array(l.bias_m, outputs);
			l.bias_v_gpu = opencl_make_array(l.bias_v, outputs);
			l.scale_m_gpu = opencl_make_array(l.scale_m, outputs);
			l.scale_v_gpu = opencl_make_array(l.scale_v, outputs);
		}

		if (batch_normalize) {
			l.mean_gpu = opencl_make_array(l.mean, outputs);
			l.variance_gpu = opencl_make_array(l.variance, outputs);

			l.rolling_mean_gpu = opencl_make_array(l.rolling_mean, outputs);
			l.rolling_variance_gpu = opencl_make_array(l.rolling_variance, outputs);

			l.mean_delta_gpu = opencl_make_array(l.mean_delta, outputs);
			l.variance_delta_gpu = opencl_make_array(l.variance_delta, outputs);

			l.scales_gpu = opencl_make_array(l.scales, outputs);
			l.scale_updates_gpu = opencl_make_array(l.scale_updates, outputs);

			l.x_gpu = opencl_make_array(l.output, l.batch * outputs);
			l.x_norm_gpu = opencl_make_array(l.output, l.batch * outputs);
		}
	}
#endif
	l.activation = activation;
	fprintf(stderr, "connected                            %4d  ->  %4d\n", inputs, outputs);
	return l;
}

void update_connected_layer(layer l, update_args a)
{
	float learning_rate = a.learning_rate*l.learning_rate_scale;
	float momentum = a.momentum;
	float decay = a.decay;
	int batch = a.batch;
	axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
	scal_cpu(l.outputs, momentum, l.bias_updates, 1);

	if(l.batch_normalize){
		axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
		scal_cpu(l.outputs, momentum, l.scale_updates, 1);
	}

	axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
	axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
	scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
}

void forward_connected_layer(layer l, network net)
{
	fill_cpu(l.outputs*l.batch, 0, l.output, 1);
	int m = l.batch;
	int k = l.inputs;
	int n = l.outputs;
	float *a = net.input;
	float *b = l.weights;
	float *c = l.output;
	gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
	if(l.batch_normalize){
		forward_batchnorm_layer(l, net);
	} else {
		add_bias(l.output, l.biases, l.batch, l.outputs, 1);
	}
	activate_array(l.output, l.outputs*l.batch, l.activation);
}

void backward_connected_layer(layer l, network net)
{
	gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);

	if(l.batch_normalize){
		backward_batchnorm_layer(l, net);
	} else {
		backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1);
	}

	int m = l.outputs;
	int k = l.batch;
	int n = l.inputs;
	float *a = l.delta;
	float *b = net.input;
	float *c = l.weight_updates;
	gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);

	m = l.batch;
	k = l.outputs;
	n = l.inputs;

	a = l.delta;
	b = l.weights;
	c = net.delta;

	if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}


void denormalize_connected_layer(layer l)
{
	int i, j;
	for(i = 0; i < l.outputs; ++i){
		float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001);
		for(j = 0; j < l.inputs; ++j){
			l.weights[i*l.inputs + j] *= scale;
		}
		l.biases[i] -= l.rolling_mean[i] * scale;
		l.scales[i] = 1;
		l.rolling_mean[i] = 0;
		l.rolling_variance[i] = 1;
	}
}


void statistics_connected_layer(layer l)
{
	if(l.batch_normalize){
		printf("Scales ");
		print_statistics(l.scales, l.outputs);
		/*
		   printf("Rolling Mean ");
		   print_statistics(l.rolling_mean, l.outputs);
		   printf("Rolling Variance ");
		   print_statistics(l.rolling_variance, l.outputs);
		 */
	}
	printf("Biases ");
	print_statistics(l.biases, l.outputs);
	printf("Weights ");
	print_statistics(l.weights, l.outputs);
}

#ifdef GPU

void pull_connected_layer(layer l)
{
	opencl_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
	opencl_pull_array(l.biases_gpu, l.biases, l.outputs);
	opencl_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
	opencl_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
	if (l.batch_normalize){
		opencl_pull_array(l.scales_gpu, l.scales, l.outputs);
		opencl_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
		opencl_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
	}
}

void push_connected_layer(layer l)
{
	opencl_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
	opencl_push_array(l.biases_gpu, l.biases, l.outputs);
	opencl_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
	opencl_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
	if (l.batch_normalize){
		opencl_push_array(l.scales_gpu, l.scales, l.outputs);
		opencl_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
		opencl_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
	}
}

void update_connected_layer_gpu(layer l, update_args a)
{
	float learning_rate = a.learning_rate*l.learning_rate_scale;
	float momentum = a.momentum;
	float decay = a.decay;
	int batch = a.batch;
	if(a.adam){
		adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t);
		adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
		if(l.scales_gpu.ptr){
			adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
		}
	}else{
		axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
		scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);

		if(l.batch_normalize){
			axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
			scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1);
		}

		axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
		axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
		scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
	}
}

void forward_connected_layer_gpu(layer l, network net)
{
	fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);

	int m = l.inputs;
	int k = l.batch;
	int n = l.outputs;

	cl_mem_ext a = net.input_gpu;
	cl_mem_ext b = l.weights_gpu;
	cl_mem_ext c = l.output_gpu;

	gemm_offset_gpu(0,1,k,n,m,1,a,0,m,b,0,m,1,c,0,n);

	if (l.batch_normalize) {
		forward_batchnorm_layer_gpu(l, net);
	} else {
		add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1);
	}
	activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
}

void backward_connected_layer_gpu(layer l, network net)
{
	constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
	gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
	if(l.batch_normalize){
		backward_batchnorm_layer_gpu(l, net);
	} else {
		backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1);
	}

	int m = l.outputs;
	int k = l.batch;
	int n = l.inputs;

	cl_mem_ext a = l.delta_gpu;
	cl_mem_ext b = net.input_gpu;
	cl_mem_ext c = l.weight_updates_gpu;

	gemm_offset_gpu(1,0,m,n,k,1,a,0,m,b,0,n,1,c,0,n);

	m = l.batch;
	k = l.outputs;
	n = l.inputs;

	a = l.delta_gpu;
	b = l.weights_gpu;
	c = net.delta_gpu;

	if(c.ptr) gemm_offset_gpu(0,0,m,n,k,1,a,0,k,b,0,n,1,c,0,n);
}
#endif
